{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ea5028a5-e497-43b9-95d9-d54b9d559a0f",
    "_uuid": "e2e867859acbb5fc5b62f8a8a59439f3d1d7746c"
   },
   "source": [
    "# 使用决策树和随机森林预测员工离职率\n",
    "\n",
    "我们的任务是帮助人事部门理解员工为何离职, 预测一个员工离职的可能性.\n",
    "数据来源: https://www.kaggle.com/ludobenistant/hr-analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a46b6c64-6f25-4007-9f41-4abd863b0130",
    "_uuid": "db8d6c6327671959fae8eefb9e3e60adad46afbe"
   },
   "source": [
    "# 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "9b79a352-5901-4b5a-af72-c2872c6c2d3b",
    "_uuid": "4ebdadf89270fbbe8a2efa6aa8053c3605fbc808"
   },
   "outputs": [],
   "source": [
    "# 引入工具包\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as matplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "af5af896-7e0a-46fb-837a-b6f0ae51fd56",
    "_uuid": "4bbc946c081a0d297fd5801048e9b49a0d036168"
   },
   "outputs": [],
   "source": [
    "# 读入数据到Pandas Dataframe \"df\"\n",
    "df = pd.read_csv('HR_comma_sep.csv', index_col=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "88cf1176-a3c4-49a9-915c-3bea5c8a3a4e",
    "_uuid": "b5b800ca16faed7e7317413bd6fe0c4c44d082fb"
   },
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "def1f3a5-2295-4cfa-8000-5e501cc15864",
    "_uuid": "b31aed21b4ffe7dd68ecf96412d26a0f58425ce6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       False\n",
       "last_evaluation          False\n",
       "number_project           False\n",
       "average_montly_hours     False\n",
       "time_spend_company       False\n",
       "Work_accident            False\n",
       "left                     False\n",
       "promotion_last_5years    False\n",
       "sales                    False\n",
       "salary                   False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检测是否有缺失数据\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "9b322ef9-e81d-451e-97e9-8d07565c3926",
    "_uuid": "d4e87b49f2d638cb7d2f64b830ed835fd9790412",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据的样例\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "d59ec584-237a-492f-aca7-a5302571b3ca",
    "_uuid": "d26af4fb750db2dddd884c2294803f4e9e3a157b"
   },
   "outputs": [],
   "source": [
    "# 重命名\n",
    "df = df.rename(columns={'satisfaction_level': 'satisfaction', \n",
    "                        'last_evaluation': 'evaluation',\n",
    "                        'number_project': 'projectCount',\n",
    "                        'average_montly_hours': 'averageMonthlyHours',\n",
    "                        'time_spend_company': 'yearsAtCompany',\n",
    "                        'Work_accident': 'workAccident',\n",
    "                        'promotion_last_5years': 'promotion',\n",
    "                        'sales' : 'department',\n",
    "                        'left' : 'turnover'\n",
    "                        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "a8a74097-4ab3-4570-bdff-7b911fe3368f",
    "_uuid": "0e8ced3df6e4031c4ae2265bd192a55334065a92",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>turnover</th>\n",
       "      <th>satisfaction</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>projectCount</th>\n",
       "      <th>averageMonthlyHours</th>\n",
       "      <th>yearsAtCompany</th>\n",
       "      <th>workAccident</th>\n",
       "      <th>promotion</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   turnover  satisfaction  evaluation  projectCount  averageMonthlyHours  \\\n",
       "0         1          0.38        0.53             2                  157   \n",
       "1         1          0.80        0.86             5                  262   \n",
       "2         1          0.11        0.88             7                  272   \n",
       "3         1          0.72        0.87             5                  223   \n",
       "4         1          0.37        0.52             2                  159   \n",
       "\n",
       "   yearsAtCompany  workAccident  promotion department  salary  \n",
       "0               3             0          0      sales     low  \n",
       "1               6             0          0      sales  medium  \n",
       "2               4             0          0      sales  medium  \n",
       "3               5             0          0      sales     low  \n",
       "4               3             0          0      sales     low  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将预测标签‘是否离职’放在第一列\n",
    "front = df['turnover']\n",
    "df.drop(labels=['turnover'], axis=1, inplace = True)\n",
    "df.insert(0, 'turnover', front)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "11e56653-4ce4-44eb-b562-a5715562c3cc",
    "_uuid": "7af6d7934b3f0212c93d1a07e69f934dd986a954"
   },
   "source": [
    "## 分析数据\n",
    " - 14999 条数据, 每一条数据包含 10 个特征 \n",
    " - 总的离职率 24%\n",
    " - 平均满意度 0.61"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "73419440-c92d-4f6a-a718-216646b900cc",
    "_uuid": "a7eabf82ee6adbd09aca9e878112f8525ea50417"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "25bc8dfe-5d24-4249-bab0-0424a20b1bd5",
    "_uuid": "4be89ba5d2e7f693b0cd228830423a700b6196fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "turnover                 int64\n",
       "satisfaction           float64\n",
       "evaluation             float64\n",
       "projectCount             int64\n",
       "averageMonthlyHours      int64\n",
       "yearsAtCompany           int64\n",
       "workAccident             int64\n",
       "promotion                int64\n",
       "department              object\n",
       "salary                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征数据类型. \n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "bbac7b26-fc6d-47ad-8b15-b1f61191a471",
    "_uuid": "e4a3109a02643d0a8388553ee176f7fc112f321a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.761917\n",
       "1    0.238083\n",
       "Name: turnover, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turnover_rate = df.turnover.value_counts() / len(df)\n",
    "turnover_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "bc3d99b1-7bc5-4ba2-a34f-0a721c51075f",
    "_uuid": "a9ae5674c17ee2b88035aef24d674c56d0b89a03",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>turnover</th>\n",
       "      <th>satisfaction</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>projectCount</th>\n",
       "      <th>averageMonthlyHours</th>\n",
       "      <th>yearsAtCompany</th>\n",
       "      <th>workAccident</th>\n",
       "      <th>promotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.238083</td>\n",
       "      <td>0.612834</td>\n",
       "      <td>0.716102</td>\n",
       "      <td>3.803054</td>\n",
       "      <td>201.050337</td>\n",
       "      <td>3.498233</td>\n",
       "      <td>0.144610</td>\n",
       "      <td>0.021268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.425924</td>\n",
       "      <td>0.248631</td>\n",
       "      <td>0.171169</td>\n",
       "      <td>1.232592</td>\n",
       "      <td>49.943099</td>\n",
       "      <td>1.460136</td>\n",
       "      <td>0.351719</td>\n",
       "      <td>0.144281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           turnover  satisfaction    evaluation  projectCount  \\\n",
       "count  14999.000000  14999.000000  14999.000000  14999.000000   \n",
       "mean       0.238083      0.612834      0.716102      3.803054   \n",
       "std        0.425924      0.248631      0.171169      1.232592   \n",
       "min        0.000000      0.090000      0.360000      2.000000   \n",
       "25%        0.000000      0.440000      0.560000      3.000000   \n",
       "50%        0.000000      0.640000      0.720000      4.000000   \n",
       "75%        0.000000      0.820000      0.870000      5.000000   \n",
       "max        1.000000      1.000000      1.000000      7.000000   \n",
       "\n",
       "       averageMonthlyHours  yearsAtCompany  workAccident     promotion  \n",
       "count         14999.000000    14999.000000  14999.000000  14999.000000  \n",
       "mean            201.050337        3.498233      0.144610      0.021268  \n",
       "std              49.943099        1.460136      0.351719      0.144281  \n",
       "min              96.000000        2.000000      0.000000      0.000000  \n",
       "25%             156.000000        3.000000      0.000000      0.000000  \n",
       "50%             200.000000        3.000000      0.000000      0.000000  \n",
       "75%             245.000000        4.000000      0.000000      0.000000  \n",
       "max             310.000000       10.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示统计数据\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "d61efd5d-5c2c-4b3c-b2ac-22d562c9e91a",
    "_uuid": "cfb764723207606d3e7fe991b17cd648e04c0a7a"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>projectCount</th>\n",
       "      <th>averageMonthlyHours</th>\n",
       "      <th>yearsAtCompany</th>\n",
       "      <th>workAccident</th>\n",
       "      <th>promotion</th>\n",
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       "    <tr>\n",
       "      <th>turnover</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>0.666810</td>\n",
       "      <td>0.715473</td>\n",
       "      <td>3.786664</td>\n",
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       "      <td>3.380032</td>\n",
       "      <td>0.175009</td>\n",
       "      <td>0.026251</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.440098</td>\n",
       "      <td>0.718113</td>\n",
       "      <td>3.855503</td>\n",
       "      <td>207.419210</td>\n",
       "      <td>3.876505</td>\n",
       "      <td>0.047326</td>\n",
       "      <td>0.005321</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          satisfaction  evaluation  projectCount  averageMonthlyHours  \\\n",
       "turnover                                                                \n",
       "0             0.666810    0.715473      3.786664           199.060203   \n",
       "1             0.440098    0.718113      3.855503           207.419210   \n",
       "\n",
       "          yearsAtCompany  workAccident  promotion  \n",
       "turnover                                           \n",
       "0               3.380032      0.175009   0.026251  \n",
       "1               3.876505      0.047326   0.005321  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分组的平均数据统计\n",
    "turnover_Summary = df.groupby('turnover')\n",
    "turnover_Summary.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "918f20ff-ed2c-43dd-849c-b7d751aeed86",
    "_uuid": "bed79decbf6495197f8f0219212c41c0042e196e"
   },
   "source": [
    "##  相关性分析\n",
    "***\n",
    "**正相关的特征:** \n",
    "- projectCount VS evaluation: 0.349333\n",
    "- projectCount VS averageMonthlyHours:  0.417211\n",
    "- averageMonthlyHours VS evaluation: 0.339742\n",
    "\n",
    "**负相关的特征:**\n",
    " - satisfaction VS turnover:  -0.388375\n",
    "\n",
    "**思考:**\n",
    "- 什么特征的影响最大?\n",
    "- 什么特征之间相关性最大?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "20d2f962-fcfa-434c-88ab-12dc05071b03",
    "_uuid": "1e2cb2cce7d8edf2ecd13a7197063ef3eea8d073",
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>turnover</th>\n",
       "      <th>satisfaction</th>\n",
       "      <th>evaluation</th>\n",
       "      <th>projectCount</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>turnover</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.006567</td>\n",
       "      <td>0.023787</td>\n",
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       "      <td>-0.154622</td>\n",
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       "    </tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>evaluation</th>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.105021</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.131591</td>\n",
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       "      <th>projectCount</th>\n",
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       "      <td>-0.004741</td>\n",
       "      <td>-0.006064</td>\n",
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       "    <tr>\n",
       "      <th>averageMonthlyHours</th>\n",
       "      <td>0.071287</td>\n",
       "      <td>-0.020048</td>\n",
       "      <td>0.339742</td>\n",
       "      <td>0.417211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127755</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>-0.003544</td>\n",
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       "    <tr>\n",
       "      <th>yearsAtCompany</th>\n",
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       "      <td>0.067433</td>\n",
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       "      <td>0.058697</td>\n",
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       "      <td>-0.004741</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.039245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promotion</th>\n",
       "      <td>-0.061788</td>\n",
       "      <td>0.025605</td>\n",
       "      <td>-0.008684</td>\n",
       "      <td>-0.006064</td>\n",
       "      <td>-0.003544</td>\n",
       "      <td>0.067433</td>\n",
       "      <td>0.039245</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                     turnover  satisfaction  evaluation  projectCount  \\\n",
       "turnover             1.000000     -0.388375    0.006567      0.023787   \n",
       "satisfaction        -0.388375      1.000000    0.105021     -0.142970   \n",
       "evaluation           0.006567      0.105021    1.000000      0.349333   \n",
       "projectCount         0.023787     -0.142970    0.349333      1.000000   \n",
       "averageMonthlyHours  0.071287     -0.020048    0.339742      0.417211   \n",
       "yearsAtCompany       0.144822     -0.100866    0.131591      0.196786   \n",
       "workAccident        -0.154622      0.058697   -0.007104     -0.004741   \n",
       "promotion           -0.061788      0.025605   -0.008684     -0.006064   \n",
       "\n",
       "                     averageMonthlyHours  yearsAtCompany  workAccident  \\\n",
       "turnover                        0.071287        0.144822     -0.154622   \n",
       "satisfaction                   -0.020048       -0.100866      0.058697   \n",
       "evaluation                      0.339742        0.131591     -0.007104   \n",
       "projectCount                    0.417211        0.196786     -0.004741   \n",
       "averageMonthlyHours             1.000000        0.127755     -0.010143   \n",
       "yearsAtCompany                  0.127755        1.000000      0.002120   \n",
       "workAccident                   -0.010143        0.002120      1.000000   \n",
       "promotion                      -0.003544        0.067433      0.039245   \n",
       "\n",
       "                     promotion  \n",
       "turnover             -0.061788  \n",
       "satisfaction          0.025605  \n",
       "evaluation           -0.008684  \n",
       "projectCount         -0.006064  \n",
       "averageMonthlyHours  -0.003544  \n",
       "yearsAtCompany        0.067433  \n",
       "workAccident          0.039245  \n",
       "promotion             1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18432ac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 相关性矩阵\n",
    "corr = df.corr()\n",
    "#corr = (corr)\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)\n",
    "\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "2e1c6ff1-1140-4892-b928-03df51d88fae",
    "_uuid": "90b9a8841cbc9c404923acf05a7f607c6df05bb6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "未离职员工满意度: 0.666809590479516\n",
      "离职员工满意度: 0.44009801176140917\n"
     ]
    }
   ],
   "source": [
    "# 比较离职和未离职员工的满意度\n",
    "emp_population = df['satisfaction'][df['turnover'] == 0].mean()\n",
    "emp_turnover_satisfaction = df[df['turnover']==1]['satisfaction'].mean()\n",
    "\n",
    "print( '未离职员工满意度: ' + str(emp_population))\n",
    "print( '离职员工满意度: ' + str(emp_turnover_satisfaction) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1a35ccd8-f60d-42d0-afb5-f8c32e01925a",
    "_uuid": "797103751f3468f19e22b0b9bdfb97a46737a30c"
   },
   "source": [
    "### 进行 T-Test\n",
    "***\n",
    "进行一个 t-test, 看离职员工的满意度是不是和未离职员工的满意度明显不同\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "3ce92ea4-829d-45bc-8547-ab4b1f261684",
    "_uuid": "7d9177f88ffe8bf88148390454b00abc00195467"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=-51.3303486754725, pvalue=0.0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "stats.ttest_1samp(a = df[df['turnover']==1]['satisfaction'], # 离职员工的满意度样本\n",
    "                  popmean = emp_population)  # 未离职员工的满意度均值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9a021234-21cb-4194-9179-b9ba373912cd",
    "_uuid": "a4abedc33125e59fd0fb368cd10d612ff09c6b80"
   },
   "source": [
    "T-Test 显示pvalue **(0)** 非常小, 所以他们之间是显著不同的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "207f67ca-f12f-4b20-a649-85b8dfeac3f2",
    "_uuid": "da9473ae9e6fc971e89ac291e57f208526ab58d1",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The t-分布 左边界: -1.9606285215955626\n",
      "The t-分布 右边界: 1.9606285215955621\n"
     ]
    }
   ],
   "source": [
    "degree_freedom = len(df[df['turnover']==1])\n",
    "\n",
    "LQ = stats.t.ppf(0.025,degree_freedom)  # 95%致信区间的左边界\n",
    "\n",
    "RQ = stats.t.ppf(0.975,degree_freedom)  # 95%致信区间的右边界\n",
    "\n",
    "print ('The t-分布 左边界: ' + str(LQ))\n",
    "print ('The t-分布 右边界: ' + str(RQ))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "c83e7515-15b7-42c5-b4a2-79d4b7c40086",
    "_uuid": "ad1e101b036b2bd2ac1ee0eb148b46ea8978ef3c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yuanyuan/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a19e26898>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19de4358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 概率密度函数估计\n",
    "fig = plt.figure(figsize=(15,4),)\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 0),'evaluation'] , color='b',shade=True,label='no turnover')\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 1),'evaluation'] , color='r',shade=True, label='turnover')\n",
    "ax.set(xlabel='Employee Evaluation', ylabel='Frequency')\n",
    "plt.title('Employee Evaluation Distribution - Turnover V.S. No Turnover')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "856a761b-b0a1-46e2-a751-58ad923bd5b3",
    "_uuid": "7e431f421027da4cd00322c0f206ce2840cc42a3",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a1a17f0b8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a02d6a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 概率密度函数估计\n",
    "fig = plt.figure(figsize=(15,4))\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 0),'averageMonthlyHours'] , color='b',shade=True, label='no turnover')\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 1),'averageMonthlyHours'] , color='r',shade=True, label='turnover')\n",
    "ax.set(xlabel='Employee Average Monthly Hours', ylabel='Frequency')\n",
    "plt.title('Employee AverageMonthly Hours Distribution - Turnover V.S. No Turnover')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "be7ff8b9-1523-43d4-8243-1443cb3b8186",
    "_uuid": "68bd32416c8feaf927c20bd17beaa8133e8836c6",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a19736438>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a202ac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 概率密度函数估计\n",
    "fig = plt.figure(figsize=(15,4))\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 0),'satisfaction'] , color='b',shade=True, label='no turnover')\n",
    "ax=sns.kdeplot(df.loc[(df['turnover'] == 1),'satisfaction'] , color='r',shade=True, label='turnover')\n",
    "plt.title('Employee Satisfaction Distribution - Turnover V.S. No Turnover')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "928c8cd9-4ae0-4f7a-b029-dea37ea19472",
    "_uuid": "22f78fd2f10e6d9f87b83e74a2c89ef21c2051bf",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report, precision_score, recall_score, confusion_matrix, precision_recall_curve\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "52a6380e-2cb1-41d9-add1-7fbb7c11a447",
    "_uuid": "2e0c1dcc07bd95aad1bd21f8068625fa656866b1"
   },
   "outputs": [],
   "source": [
    "# 将string类型转换为整数类型\n",
    "df[\"department\"] = df[\"department\"].astype('category').cat.codes\n",
    "df[\"salary\"] = df[\"salary\"].astype('category').cat.codes\n",
    "\n",
    "# 产生X, y\n",
    "target_name = 'turnover'\n",
    "X = df.drop('turnover', axis=1)\n",
    "y = df[target_name]\n",
    "\n",
    "# 将数据分为训练和测试数据集\n",
    "# 注意参数 stratify = y 意味着在产生训练和测试数据中, 离职的员工的百分比等于原来总的数据中的离职的员工的百分比\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.15, random_state=123, stratify=y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2a9855af-4efd-4692-8b60-84d6c8ddafea",
    "_uuid": "ce1f37f6997bba100650cb23814faef02d9945ea"
   },
   "source": [
    "## Decision Tree V.S. Random Forest \n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_cell_guid": "5a76ae8a-f8af-4241-aae5-c7cd5cced2ad",
    "_uuid": "3e3565867e9559ab635430bf2045927a5f56d31a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " ---决策树---\n",
      "决策树 AUC = 0.93\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.98      0.97      1714\n",
      "           1       0.93      0.89      0.91       536\n",
      "\n",
      "   micro avg       0.96      0.96      0.96      2250\n",
      "   macro avg       0.95      0.93      0.94      2250\n",
      "weighted avg       0.96      0.96      0.96      2250\n",
      "\n",
      "\n",
      "\n",
      " ---随机森林---\n",
      "随机森林 AUC = 0.97\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99      1714\n",
      "           1       0.99      0.94      0.96       536\n",
      "\n",
      "   micro avg       0.98      0.98      0.98      2250\n",
      "   macro avg       0.99      0.97      0.98      2250\n",
      "weighted avg       0.98      0.98      0.98      2250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "\n",
    "# 决策树\n",
    "dtree = tree.DecisionTreeClassifier(\n",
    "    criterion='entropy',\n",
    "    #max_depth=3, # 定义树的深度, 可以用来防止过拟合\n",
    "    min_weight_fraction_leaf=0.01 # 定义叶子节点最少需要包含多少个样本(使用百分比表达), 防止过拟合\n",
    "    )\n",
    "dtree = dtree.fit(X_train,y_train)\n",
    "print (\"\\n\\n ---决策树---\")\n",
    "dt_roc_auc = roc_auc_score(y_test, dtree.predict(X_test))\n",
    "print (\"决策树 AUC = %2.2f\" % dt_roc_auc)\n",
    "print(classification_report(y_test, dtree.predict(X_test)))\n",
    "\n",
    "# 随机森林\n",
    "rf = RandomForestClassifier(\n",
    "    criterion='entropy',\n",
    "    n_estimators=1000, \n",
    "    max_depth=None, # 定义树的深度, 可以用来防止过拟合\n",
    "    min_samples_split=10, # 定义至少多少个样本的情况下才继续分叉\n",
    "    #min_weight_fraction_leaf=0.02 # 定义叶子节点最少需要包含多少个样本(使用百分比表达), 防止过拟合\n",
    "    )\n",
    "rf.fit(X_train, y_train)\n",
    "print (\"\\n\\n ---随机森林---\")\n",
    "rf_roc_auc = roc_auc_score(y_test, rf.predict(X_test))\n",
    "print (\"随机森林 AUC = %2.2f\" % rf_roc_auc)\n",
    "print(classification_report(y_test, rf.predict(X_test)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f37f3cd2-8dcc-41c0-a56f-91e8df1d9adb",
    "_uuid": "4eba4ea0d57b8b74f708a0ccea08c21bb72dc803"
   },
   "source": [
    "## ROC 图\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_cell_guid": "2ad3ed61-963c-430a-b150-aa0dacfa9f8f",
    "_uuid": "0eca62b259ac194a0aef17617a01ee929120bd8c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19768898>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC 图\n",
    "from sklearn.metrics import roc_curve\n",
    "rf_fpr, rf_tpr, rf_thresholds = roc_curve(y_test, rf.predict_proba(X_test)[:,1])\n",
    "dt_fpr, dt_tpr, dt_thresholds = roc_curve(y_test, dtree.predict_proba(X_test)[:,1])\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "# 随机森林 ROC\n",
    "plt.plot(rf_fpr, rf_tpr, label='Random Forest (area = %0.2f)' % rf_roc_auc)\n",
    "\n",
    "# 决策树 ROC\n",
    "plt.plot(dt_fpr, dt_tpr, label='Decision Tree (area = %0.2f)' % dt_roc_auc)\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Graph')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过决策树分析不同的特征的重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_cell_guid": "3bae8cd1-9cc5-4e5d-b3bc-82086e4867d3",
    "_uuid": "19bd16ebb1fbc343596fa25f6bbd170064c3b855"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b37d400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 画出决策树特征的重要性 ##\n",
    "importances = rf.feature_importances_\n",
    "feat_names = df.drop(['turnover'],axis=1).columns\n",
    "\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title(\"Feature importances by DecisionTreeClassifier\")\n",
    "plt.bar(range(len(indices)), importances[indices], color='lightblue',  align=\"center\")\n",
    "plt.step(range(len(indices)), np.cumsum(importances[indices]), where='mid', label='Cumulative')\n",
    "plt.xticks(range(len(indices)), feat_names[indices], rotation='vertical',fontsize=14)\n",
    "plt.xlim([-1, len(indices)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ab97fd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 画出随机森林的特征的重要性 ##\n",
    "importances = rf.feature_importances_\n",
    "feat_names = df.drop(['turnover'],axis=1).columns\n",
    "\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title(\"Feature importances by Random Forest\")\n",
    "plt.bar(range(len(indices)), importances[indices], color='lightblue',  align=\"center\")\n",
    "plt.step(range(len(indices)), np.cumsum(importances[indices]), where='mid', label='Cumulative')\n",
    "plt.xticks(range(len(indices)), feat_names[indices], rotation='vertical',fontsize=14)\n",
    "plt.xlim([-1, len(indices)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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